Estimating an effective radius of a tire of a vehicle

ABSTRACT

A method for estimating an effective radius of a tire of a vehicle, the method may include (i) obtaining sensed information that reflects (a) a distance passed by the vehicle during one or more driving sessions, (b) a rotational speed of at least a wheel that comprises the tire during the one or more driving sessions, (c) values of tire radius affecting parameters during the one or more driving sessions, wherein the tire radius affecting parameters comprise a vehicle speed and at least some other tire radius affecting parameters; (ii) selecting at least one portion of the one or more driving sessions; and (iii) determining the effective radius of the tire of the vehicle based on (a) sensed information gained during the at least one portion, the sensed information comprises values of the tire radius affecting parameters during the at least one portion, and (b) one or more relationships between the effective radius of the tire and tire radius affecting parameters.

CROSS REFERENCE

This application is a continuation in part of PCT patent applicationPCT/IB21/51794 filing date 4 Mar. 2021 which in turn claims priorityfrom US provisional patent filing date Mar. 6, 2020 Ser. No. 62/985,894and claims priority from US provisional patent filing date Mar. 6, 2020Ser. No. 62/985,899—all applications being incorporated herein in theirentirety.

This application also claims priority from U.S. provisional patent Ser.No. 63/260,907 filing date Sep. 3, 2021 that is being incorporatedherein in its entirety.

BACKGROUND

Vehicles are highly complex that include multiple components. The healthof the components may degrade over time and in many cases—they may failbefore being fixed in dedicated garages. Some failures cannot befixed—and may require replacing the entire components—or even biggerparts of the vehicle.

There is a growing need to provide extensive information about thestatus of the vehicle.

A vehicle usually has a certain number of sensors. These sensors mayprovide only partial information about the status and behaviors of thevehicle.

There is a growing need to provide additional information about thevehicle—and especially regarding to various components of thevehicles—and especially to aspects that are not directly measured bydedicated actual sensors.

DESCRIPTION OF THE DRAWINGS

The embodiments of the disclosure will be understood and appreciatedmore fully from the following detailed description, taken in conjunctionwith the drawings in which:

FIG. 1 is an example of a method;

FIG. 2A is an example of a method;

FIG. 2B is an example of a method;

FIG. 2C is an example of a method;

FIG. 3A is an example of a method;

FIG. 3B is an example of a method;

FIG. 3C is an example of a method;

FIG. 3D is an example of a method;

FIG. 3E is an example of a method;

FIG. 3F is an example of a method;

FIG. 3G is an example of a method;

FIG. 4A is an example of a method;

FIG. 4B is an example of a method;

FIG. 5 is an example of a method; and

FIG. 6 is an example of a vehicle and a remote system.

DETAILED DESCRIPTION OF THE DRAWINGS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

Any reference in the specification to a system should be applied mutatismutandis to a method that can be executed by the system.

Because the illustrated embodiments of the present invention may for themost part, be implemented using electronic components and circuits knownto those skilled in the art, details will not be explained in anygreater extent than that considered necessary as illustrated above, forthe understanding and appreciation of the underlying concepts of thepresent invention and in order not to obfuscate or distract from theteachings of the present invention.

Any reference in the specification to a method should be applied mutatismutandis to a system capable of executing the method and should beapplied mutatis mutandis to a non-transitory computer readable mediumthat stores instructions that once executed by a computer result in theexecution of the method.

Any reference in the specification to a system should be applied mutatismutandis to a method that can be executed by the system and should beapplied mutatis mutandis to a non-transitory computer readable mediumthat stores instructions that once executed by a computer result in theexecution of the method.

Any of the mentioned below methods, devices and/or computer readablemedium may use one or more aspects and/or components and/or steps and/orinstructions illustrated in at least one of the followingapplications—all being incorporated herein in its entirety:

-   a. US provisional patent 62/573,828 filing date Oct. 18, 2017.-   b. US provisional patent 62/556,447 filing date Sep. 10, 2017.-   c. US provisional patent 62/556,445 filing date Sep. 10, 2017.-   d. US provisional patent 62/564,270 filing date Sep. 28, 2017.-   e. US provisional patent 62/575,544 filing date Oct. 23, 2017.-   f. US provisional patent 62/556,444 filing date Sep. 10, 2017.-   g. US provisional patent 62/556,443 filing date Sep. 10, 2017.-   h. US provisional patent 62/722,210 filing date Aug. 24, 2018.-   i. PCT patent application PCT/IB2018/056844 filing date Sep. 7,    2019.

VCV and Aggregate Damage

The following terms and/or phrases are used in the specification:

-   a. And/or—additionally or alternatively. “A and/or B” may mean only    A, may mean only B, and may mean A and B.-   b. Multi Component Module (MCM)—a set of components that implements    one of the vehicle's systems (e.g. suspension, braking, etc.).-   c. MCM behavioral affecting parameters—a parameter that affects the    behavior of the MCM. The parameter may be a parameter of the entire    vehicle (for example weight, speed), a parameter of a components    that does not belong to the MCM (for example an effective radius of    a tire), and/or one or more environmental parameter (for example a    temperature). The MCM behavioral affecting parameter differs from    the health or status of the MCM.-   d. Component behavioral affecting parameters—a parameter that    affects the behavior of the component. The parameter may be a    parameter of the entire vehicle (for example weight, speed), a    parameter of a components that does not belong to the component (for    example an effective radius of a tire), and/or one or more    environmental parameter (for example a temperature). The component    behavioral affecting parameter differs from the health or status of    the component.-   e. Comparable Vehicle Class (CVC)—vehicles of the CVC are comparable    to each other—for example of the same year and model and    manufacturer—have the same engine (when engine is compared), and the    like.-   f. Vehicle CV (VCV) is a record of information describing current    vehicle status and its history, regarding vehicle    multi-components-modules (MCMs) health and performance. The    information is objective per a specific vehicle, as well as    relative, compering to similar make and model.-   g. A MCM driving event—a driving event that may be selected in order    to provide information regarding a status of an MCM. For    example—assuming that the MCM is the chassis of the vehicle then the    driving event may include driving over a bump or entering a    pothole—as the MCM is expected to affect the movement of the vehicle    while passing the bump and/or while entering a pothole.-   h. A component driving event—a driving event that may be selected in    order to provide information regarding a status of a component. For    example—assuming that the component is one or more bushings, then    the component event may include turning the vehicle—as a defective    bushing is expected to introduce random movement of the wheels in    relation to the steering wheels.

There may be provided a method for evaluating the status of variousunits of the vehicle. The evaluation may be component-based or may bemulti-component-module (MCM)-based. In an MCM the status of the wholeMCM may be provided and/or the status of the separate components of theMCM may be provided.

For example—a suspension may be an MCM—and its status as a whole may beprovided. Additionally or alternatively—the status of the suspensioncomponents may be provided—for example, the status of an air spring,dumper, bushes and the like.

The status of a component and/or of an MCM may be evaluated by obtainingsensed information during one or more driving sessions of a vehicle andprocessing the information. The processing may be executed by thevehicle, and/or may be executed at least in part by another computerizedsystem located outside the vehicle.

The processing may include compensating for the impact of behavioraffecting parameters on the behavior of vehicle components and/orvehicle MCMs. The impact may be learnt by obtaining and processing largeamount of information (for example using big data technologies), machinelearning and the like. Examples of obtaining and processing large amountof information are illustrated in PCT patent applicationPCT/IB2018/056844.

By learning the impact of driving events on the lifespan and/or state ofthe components and/or MCMs—the method may predict future faults andprovide an indication of fault prevention steps to be taken—even wellbefore the failure occurs.

The method may provide a wealth of information of the status of thevehicle, of multiple components of the vehicle and or one or moreMCMs—of the vehicles.

The status of components and/or MCMs may be arranged in variousmanners—for example may be gathered in so-called Vehicle CurriculumVitae (VCV)—information suite describing vehicle history and currentstatus, regarding health and its characteristics and performance of itsdifferent MCMs. Any other arrangement of information may be provided.

When sensed information is gathered from different sensors—it may becorrelated in the sense that sensed information obtained by one sensorat a certain point of time will be processed with (and/or compared toand/or used to validate) sensed information obtained by another sensorat the certain point of time. The correlation may result in associatingsensed information sensed by different sensors but related to the sameevent.

The VCV may be aimed to provide the vehicle owner, the vehiclemanufacturer, service provides, and the like, meaningful insights aboutthe health and performance of a specific vehicle, for better:

-   a. Condition evaluation.-   b. Diagnostics for maintenance.-   c. Improvements for future model.

The MCMs may include at least one of the following:

-   a. Suspension.-   b. Camber, Toe and Caster.-   c. Engine.-   d. Chassis.-   e. Vehicle body.-   f. Tires.-   g. Brakes.-   h. Steering system.-   i. Battery.-   j. Gear/transmission.

Following are the detailed aspects for characterizing the MCMs. For eachof them, analyze their performance correlated with:

-   a. Surface information: for example grade, inclination, curvature,    roughness, friction, distresses as stimulations. Examples of surface    information are illustrated in PCT patent application    PCT/IB2018/056844 filing date Mar. 14, 2019 which is incorporated    herein by reference.-   b. Other vehicle sensors. The other sensors may be physical (also    referred to as actual) sensors and/or virtual sensors.-   c. Sensed information from other vehicles.

Each MCM may be analyzed as a whole unit and the method may proceed toanalyze components of the MCM.

Suspension

A suspension should maximize the friction between the tires and the roadsurface, to provide steering stability with good handling, and to ensurethe comfort of the passengers.

The method may analyze the suspension as an MCM.

The method may assess performance and health of the different suspensionMCM components:

-   a. Air spring.-   b. Dumper.-   c. Links.-   d. Bushing-   e. Anti-Roll Bar.-   f. Metal spring.

The method may include analyzing general performance of the suspensionsas a whole system, and may correlate this with a stimulation (drivingevent that will result in a response of the suspension).

Any of the following items (signature) may be normalized, may becompared to other signatures, and may be further processed to provideone or more scores. For simplicity of explanation some of these stepsmay be omitted.

Impulse response—this tests the “Ride” aspect—ability to dump/restrainimpulse type of excitations.

The MCM driving events (also referred to as trigger or excitation) mayinclude:

-   a. Step-up impulse: climb out of a pothole, or climb on top of a    bump.-   b. Step-down impulse: drop into a pothole, or drop from a bump.

The method may include characterizing the impulse-excitation—magnitudeand nature.

The method may include normalization that should take into account (forexample) MCM behavioral affecting parameters such as wheel speed, weightof the vehicle, torque (higher torque will reduce the effect of thestep-up impulse), acceleration, grade of the road etc.

The method may measure the resulted effect on the chassis/drivermovements:

-   a. Use accelerometer attached to chassis—2D or 3D.-   b. Wheel speed.-   c. Measure “Bump steer” effect.

The method may characterize the impulse response to generate asignature.

This may include time to frequency conversion (such as Fast FourierTransform), calculating response features, and generating a signaturesuch as an impulse-response-signature, describing: impulse-excitation,response, bounce, etc. The signature should represents the impulseresponse—for example amplitude of speed change, duration of speedoscillations, and the like.

The signature may be compared to signatures of older responses of thesame vehicle and/or compared to signatures of responses of othervehicles in the same CVC.

The method may include calculating at least one out of:

-   a. Suspension Step-up response score—per velocity.-   b. Suspension Step-down Response score.

Any of these scores may be based on x-axis and z-axis accelerationsignals (Ax and Az) during a period (for example, a few seconds forexample ten seconds) following the impulse excitation. Theseacceleration signals may be processed to determine the average value ofthe dumping factor and the peak-to peak (amplitude) of the response.

The score may be the signature or may apply any function on thesignature.

The score may represent the normalized impulse response in any manner.

Serpentine Response.

The MCM driving event may include fast mass transfer from side to side.It may be detected by sensing sharp steering to one side followedimmediately by sharp steering to the other side. Sharp—above apredefined threshold—for example more than X degrees per second.

The method may include characterizing the serpentine-excitation bymagnitude and nature.

Normalization should take into account (for example) MCM behavioralaffecting parameters such as wheel speed, torque, acceleration, roadgrade, etc.

The method may measure the resulted effect on the chassis/drivermovements:

-   a. Use accelerometer attached to chassis—2D or 3D.-   b. Wheel speed.-   c. Deflection sensors.

The method may characterize the serpentine response to generate asignature.

This may include time to frequency conversion (such as Fast FourierTransform), calculating response features, and generating a signaturesuch as a signature that may describe serpentine-excitation vector,response, bounce, etc.

The signature may be compared to signatures of older responses of thesame vehicle and/or compared to signatures of responses of othervehicles in the same CVC.

The method may include calculating at least one out of:

-   a. Right side suspension serpentine response score.-   b. Left side suspension serpentine response.-   c. As the chassis is expected to act against the tendency to lean to    the outer side of the serpentine, we measure: Ay, Az, and V. Then    compensate for road banking. Then calculate the vehicle banking.-   d. The score gets higher as the banking gets lower.

Continues Response and Quasi-Continues Responses.

This may include a few MCM driving events such as: Cornering,acceleration squat, breaking and driving on a rough road.

Cornering may be the amount of centrifugal force the vehicle cangenerate measured in g's, and expressed as Lateral Acceleration.Cornering is a measurement of the force exerted on the vehicle's centerof gravity. For example measuring the angle of the vehicle to the roadwhen driving in a curved segment of the road.

Acceleration—results in a squat motion—when accelerating the front ofthe vehicle is elevated and the rear of the vehicle is lowered. This maybe normalized by various parameters such as (road grade, inclination) ordynamic/quasi-continues (acceleration).

Braking—results in a dive motion. When decelerating the front of thevehicle is lowered and the rear of the vehicle is lifted.

Rough road—a “frequent” set of impulse excitations.

The method may include characterizing the continues-excitation: natureand magnitude.

This may be followed by normalizing by MCM behavioral affectingparameters such as wheel speed, torque, accelerometer, grade, radius,banking, roughness index, acceleration.

The method may include measuring the resulted effect on thechassis/driver movements:

-   a. Use accelerometer attached to chassis—2D or 3D.-   b. Use the different HW sensors of suspension MCM: air-spring,    dumper, etc.

The method may include measuring the resulted effect on the grip:

-   a. Braking—grip loss on rear wheels.-   b. Acceleration—grip loss on front wheels.-   c. Cornering right/left—grip loss on left/right wheels,    over-steer/under-steer.-   d. Rough road—grip loss on all wheels, correlated to driving speed.-   e. Measure “ride height”—During the continues-excitation.

Characterize the continues-response:

-   a. Calculate: chassis position, relative to surface and Earth, at    the 4 corners-   b. Using: features, processed HW sensors, etc.-   c. Generate a continues-response-signature, describing:    continues-excitation, response.-   d. Normalize to comparable values, using: speed, weight, and    continues operation duration (that heat the dumpers).

The signature may be compared to signatures of older responses of thesame vehicle and/or compared to signatures of responses of othervehicles in the same CVC.

Generate performance score: continuous—excitation type.

Outcomes:

-   a. Suspension Cornering Response score.-   b. Suspension Braking Response score.-   c. Suspension Acceleration Response score.-   d. Suspension Banking Response score.-   e. Suspension Roughness Response score.    The score functions reflect the tendency of the vehicle chassis to    remain parallel to earth, despite of the excitation forces.

Suspension Sub-Component: Air Springs

Air springs includes of a cylindrical chamber of air positioned betweenthe wheel and the vehicle's body, use the compressive qualities of airto absorb wheel vibrations. Electrically controlled air-springs are usedalso to lift the vehicle or level it.

When detecting triggers/excitations/events (component driving event):

-   a. Hard accelerations/braking-   b. Air compressor/releasing—valve activation-   c. Deflection sensor changes during idle periods, or quasi-static    excitations

Characterize the excitation magnitude and nature.

Normalizing by compensating for component behavioral affectingparameters such as speed, acceleration/deceleration, road grade, weightetc.

Measure the resulted effect on the chassis/driver movements:

-   a. Use accelerometer attached to chassis—2D or 3D, deflection    sensors, valve/compressor, pressure, etc.-   b. Measure deflection and “quasi-static” acceleration changes

Characterize the performance and response for air leak, by:

-   a. Aggregate the compressor “abnormal” activation period, ignoring    the periods where it should work (i.e. when air spring needs    control). I.e. focus on “smooth” driving sections.-   b. Monitor deflection sensor changes when compressor is off, and    vehicle is standing.-   c. Evaluate working area/height (i.e. dynamic range of the springs).

Monitor min/max deflection values, correlated with quasi-staticexcitations.

Characterize the performance and response for control loopcharacteristics by

-   a. Activating compressor and releasing-valve in reaction to changes.-   b. Monitor “nose dive” during hard braking.-   c. Fuse deflection sensors and or acceleration Ax, Az with brake    torque-   d. Normalize with: road grade, weight.-   e. Monitor “rear squat” during hard acceleration—the same as above.

Normalization—weight

Generate “signatures”:

-   a. Air leak amount—per excitations/period—aggregate the air    compressor working time in comparison to idle time. The ratio will    be the score.-   b. Working area/height—per weight, quasi-static excitation.-   F=mA=KdeltaX-   The lower the K, the higher is the score.-   c. Compressor/valve control loop—air compensation per excitation.-   d. Measure the time elapsed from the excitation moment to the    compressor or valve activation. The shorter the period the, the    higher is the score.

The signature may be compared to signatures of older responses of thesame vehicle and/or compared to signatures of responses of othervehicles in the same CVC.

Outcome scores are calculated as illustrated two paragraphs above-undergenerate “signatures” title.

-   a. Air spring system air leak score.-   b. Air spring working area score.-   c. Air spring control performance on excitations score.

Suspension Sub-Component—Damper

Springs are great at absorbing energy, but not so good at dissipatingit. Damper controls unwanted spring motion. Typically, damper have moreresistance during its extension cycle than its compression cycle—so whendriving over a bump, the spring will absorb the shock fast, but willregain its size slower.

Damper and air spring are velocity-sensitive—the faster the suspensionmoves, the more resistance the shock absorber provides. This enablesshocks to adjust to road conditions including bounce, sway, brake diveand acceleration squat.

The component driving events are impulse stimulation (step-up/step-down)

Given (normalizing factors) component behavioral affecting parameters:impulse “energy”/magnitude and type, Road grade, Weight, and Tirepressure

Characterize damper response, measure and calculate the following:

-   a. Restrain factor.-   b. Velocity response—per impulse magnitude.-   c. Differentiate between Compression cycle Vs. extension cycle.-   d. Use Deflection height Vs. Z-Acceleration, during compression and    extension cycles.-   e. Generate signatures—combined vector, Per the above response    components.

Normalize.

Generate scores per the above performance components.

The damper may be monitored using at least some of the followingsensors: body acceleration (Longitudinal, Lateral, Yaw), wheel verticalacceleration: vertical accelerometer attached to wheel axle, verticalaccelerometer on each corner of the body, deflections sensor, wheelspeed sensor, solenoid current [A] for activating one or more valves pereach dumper, air spring pressure sensor (central reservoir for all 4corners), air valve actuator (Boolean)×4 corners, compressor on/offsensor (Boolean), sensor of a multi-chamber air-spring: valve betweenthe chambers—On/off (Boolean)—controls the air-spring stiffnesscoefficient

The method may include monitoring air-spring stiffness (as illustratedtwelve paragraphs above-under generate “signatures” title), andcorrelating with the multi-chamber valve—expect different stiffnessvalue per different valve opening configuration.

Sub-Component: Bushing

Bushings are cushions made of rubber, polyurethane or other materials.They're mounted on vehicle suspension and steering joints to absorb roadbumps, control the amount of movement in the joints and reduce noise andvibration.

Bushes gets defected when rubber is warn-out or dried.

Following symptoms indicate faulty bushes:

First component driving event—may be aimed to find whether the vehiclewanders during cruise. The vehicle “tracts” to one side without steeringwheel input. Each time to a different direction and angle.

How to detect the first component driving event: monitor vehicle coursediversion by detecting difference in left/right wheel speeds, that isnot related/correlated to steering position. Defective bushing may allowrandom movement of the wheels in relation to the driving wheel.Defective bushing may result in bias towards one side of the vehicle—andin this case there is a need to check whether there is a differencebetween effective radius of wheels of a pair of wheels (pair of frontwheels or pair of rear wheels).

The method may generate a virtual sensor by generating a mapping such asa look up table (LUT) of wheel-speed difference versus steering wheelposition: magnitude and tolerance, at high resolution.

The method may analyze this mapping by looking for variations thatimplies “random” wandering and score the phenomena magnitude.

The method may generate a first score.

Second component driving event—hitting a bump or a pothole—for detectingthe steering wheel movements when hitting bump of pothole. When hittinga same impulse (step-up/step-down) on both sides. This may be detectedby monitoring a change in the steering position at the moment, andvehicle heading right after the impact.

This may be detected by:

-   a. Differentiating between driver deliberate movement of the    steering wheel, and the undesired movement (that the method may look    for): check for sharp/high frequency micro movements (assuming that    can't be done by the driver)-   b. Monitoring the undeliberate change of vehicle heading, as a    result of hitting a pothole/bump, monitor the lateral acceleration    right after a pothole or bump, correlate it with steering wheel    change.

The method may generate a virtual sensor output that may be a mapping(for example a LUT) of the above correlations.

The method may calculate a second score.

A third component driving event—may be aimed for detecting steeringwheel back1ash/hysteresis. This may involve having the vehicle divertingfrom its course, un-correlated with its steering wheel position, ormicro movement (for example −2 degrees or less) of steering wheel arenot correlated with vehicle actual course diversion.

This may be detected. When slight diversion is detected: monitordifference between speeds of left and right wheels, correlate it withsteering wheel position changes. When steering wheel movement directionis changed (e.g. from Clockwise to Counter clock wise CW), and thechange is very small, then monitor the first moments after the change ofdirection: same as above—monitor difference between speeds of left andright wheels, correlate it with steering wheel position change.

The method may generate a virtual sensor output that may be a mapping(for example a LUT) of the above correlations.

The fourth component driving event may be aimed to detect vibratingwheels.

When wheels are vibrating “randomly”—i.e. not correlated with speed orwheel frequency—therefore not related to wheel unbalance—this may be aresult of a defective bushing.

The frequency content of the vibration is high—i.e. “sharp” micromovements. The phenomena may get worse as road micro-texture is rough.

The phenomena may be detected by analyzing the wheel speed, look at thehigh-frequency components. In case vibrations are transferred to sprungmass: detect it using Accelerometer. Eliminate the noise correlated withwheel frequency. Normalize with rough roughness (an example of acomponent behavioral affecting parameter).

The method may provide a virtual sensor output that may correlate randomwheel vibrations with surface roughness—per wheel and/or per axes.

The method may generate a fourth score based on the correlation.

The method may differentiate between a “self-moving” steering wheel (dueto bad bushes) and driver move of the steering wheel. This may includedetecting load on the steering wheel motor (like used for lane-keepingmode).

Camber, Toe and Caster

This triples indicates of the position (rather orientation angle) ofvarious driving axes.

This may use a virtual sensor that provides an indication of a componentbehavioral affecting parameter such as a tire effective Radius.

The method may senses or receive an indication of whether the driver isholding the steering wheel, and what is his applied force/load.

The method may generate a signature of each phenomena (out of caster,toe and cumber).

The signature may be compared to signatures of older responses of thesame vehicle and/or compared to signatures of responses of othervehicles in the same CVC.

Caster—the caster angle is responsible for the tendency of the steeringwheel to straighten when releasing its hold right after cornering.

Change in Caster is considered as damage to chassis, and difficult tofix/compensate. Therefore very important to monitor and score.

The component driving event is aimed to detect that a steering wheelposition returns slowly or doesn't return to neutral position whenreleased after cornering.

The component driving event may include detecting a steer of the vehiclethat is followed by a release of the steering wheel.

In case of a fault—the vehicle does not return fast enough to straightdriving course.

This can be detected by monitoring, when steering wheel is releasedright after steering is done, the time/distance it take for the vehicleto return to straight driving course.

The monitoring result may be normalized by component behavioralaffecting parameters such as wheel speed, weight, tire pressure, roadgrade.

The method may include detecting the release of drive wheel by sensingpressure applied on the drive wheel.

The method may provide a virtual sensor output that may correlate thereturn distance to neutral per speed.

The method may generate a score based on the correlation. The faster thesteering wheel get to its origin position, normalized by driving speed,the higher the score is.

Toe angle is responsible for stabilizing the vehicle steering: whendriving straight and releasing the string wheel, it effects stability ofthe steering, when the driver is not holding the steering wheel. UnlikeCamber, the Toe angle faults can be fixed by garage. Therefore needs tobe monitors and presented to service personal as input information.

The component driving event is aimed to detect that a steering wheelposition returns slowly or doesn't return to neutral position whenreleased after cornering.

In case of a fault the steering wheel feels “light”, i.e. not stable, orthe driver applies micro steering “corrections” while cursing atstraight course, or the driver does not apply steering force on thesteering wheel, the vehicle is drifting to one side, or course iscorrected “too many times” from side to side.

The method may detect this by monitoring, when driving straight atcruise, and driver is not applying steering force, adiversion—difference of left and right wheel speeds.

The method may rule out difference in left/right tire pressure/effectivediameter/rolling resistance/grip.

The method may rule out bushes problem—verify a consistency in thediversion (direction and magnitude) along time—the vehicle will always“pull” to same direction with same diversion magnitude.

The monitoring result may be normalized by component behavioralaffecting parameters such as speed and surface roughness.

The method may provide a virtual sensor output that may correlatediversion/direction instability per speed.

The method may generate a score based on the correlation.

The component driving event is aimed to detect that the vehicle slipsduring curving, as the wheel is not heading the bearing (AKA slipangle).

While cornering, the steering wheel position is not exactly correlatedwith the cornering radius, even when lateral excitation is low, andsurface friction is high, and due to miss-alignment of the Ackermanangle (faulty Toe).

The method may detect this during cornering and relatively steady statehaving radius and speed, with relatively small lateral excitationmagnitude. The method may measure wheel speeds, and extract thedifference between left and right, estimate cornering radius, andcorrelate with steering wheel position.

The method may provide a virtual sensor output that may correlate thewheel speed differences (left/right/rear/forward) Vs. steering wheelposition.

The method may generate a score based on the correlation. The bigger thedifference between left/right difference and between the steering wheelposition, the lower is the score.

A component driving event may be aimed to detect that a driver needs toapply micro steering wheel movements even when driving straight (forcompensating forma faulty toe)—or to detect that while driving straightforward, the wheels steering is not stable—that may require the driverto apply micro corrections.

The method may monitor steering wheel movements when driving straightfor some period, may aggregate movements (integrate delta steering wheelpositions) and average per period.

The method may provide a virtual sensor that outputs a valuerepresenting the instability—average required movements to keep straightdriving.

Camber—camber angle mainly effects the vehicle grip while cornering. Adefect Camber angle is considered as “chassis damage” that is difficultto fix, and thus very important to monitor and score.

The component driving event may be aimed to detect a loss of lateralgrip during sharp cornering. While sharply cornering, the vehicle slipslaterally.

The method may detect this by detecting lateral slips/skids whencornering sharply.

The detected information may be normalized by component behavioralaffecting parameters such as lateral acceleration, surface friction,vehicle weight, tire pressure, surface banking/grade.

The method may differentiate between left/right cornering to detect aspecific side problem.

The method may provide a virtual sensor output that may correlate thecorrelating: lateral slip Vs. the above normalization detectedinformation.

The method may generate a score based on the correlation. The higher thedifference between grip while cornering left and right, the lower thescore is.

FIG. 1A illustrates a method 100.

Method 100 is for evaluating a status of a vehicle.

Method 100 may start by step 110 of obtaining sensed information duringone or more driving sessions of the vehicle.

A driving session may last between a few seconds, a few minutes, to oneor more days, and even more.

The obtaining may include sensing sensed information by one of moreactual sensors of the vehicle. The obtaining may also processing sensedinformation from one or more sensors (and maybe additional information)to provide sensed information of one or more virtual sensors.

The vehicle may include multiple components and some may be included inMCMs.

It is assumed, for simplicity of explanation, the method 100 evaluatesthe status of the vehicle by determining the status of one or more MCMsand the status of one or more components.

The status may be represented by one or more scores, by one or moremappings and/or look up tables, and the like.

A component (of the one or more components) may belong to an MCM (of theone or more MCMs). Alternatively—a component (of the one or morecomponents) may not belong to any of the one or more MCMs.

Step 110 may be followed by step 120.

Step 120 may include determining, based on the sensed information, (a)MCM behavioral information regarding one or more MCM driving events, and(b) a component behavioral information regarding one or more componentdriving events.

A MCM driving event is selected in order to provide informationregarding a status of an MCM. For example—assuming that the MCM is thechassis of the vehicle then the driving event may include driving over abump or entering a pothole—as the MCM is expected to affect the movementof the vehicle while passing the bump and/or while entering a pothole.

A component driving event is selected in order to provide informationregarding a status of a component. For example—assuming that thecomponent is one or more bushings, then the component event may includeturning the vehicle—as a defective bushing is expected to introducerandom movement of the wheels in relation to the steering wheels.

A behavior of at least one first part of the vehicle during the one ormore MCM driving events may be indicative of a status of one or moreMCMs. For example—assuming that the MCM is chassis and the chassis isdefective—then the defective chassis may affect the progress of theentire vehicle and/or the wheels of the vehicle.

A behavior of at least one second part of the vehicle during the one ormore component driving event may be indicative of a status of one ormore components. For example—assuming that the component is one or morebushings—and that one or more bushings are defective—then the one ormore bushings may affect the progress of the entire vehicle and/or thewheels of the vehicle.

Step 120 may be followed by step 130 and/or by step 140.

Step 130 may include determining the status of the one or more MCMs,based at least on the MCM behavioral information.

Step 130 may include step 132 of comparing the MCM behavioralinformation to another MCM behavior information.

The other MCM behavior information may be an older MCM behavioralinformation (of the same MCM of the same vehicle) regarding a previousMCM driving event—for example driving over the same bump, driving over asimilar bump, and the like.

The other MCM behavior information may be MCM behavior information of anMCM (same MCM) of another vehicle. The vehicle of the MCM and the othervehicle may belong to the same Comparable Vehicle Class.

Step 140 may include determining the status of the one or morecomponents, based at least on the component behavioral information, thestatus of the component.

Step 140 may include step 142 of comparing the component behavioralinformation to another component behavior information.

The other component behavior information may be an older componentbehavioral information (of the same component of the same vehicle)regarding a previous component driving event—for example driving overthe same bump, driving over a similar bump, and the like.

The other component behavior information may be component behaviorinformation of a component (same component) of another vehicle. Thevehicle of the component and the other vehicle may belong to the sameComparable Vehicle Class.

The sensed information may be indicative of MCM behavioral affectingparameters and wherein the method may include compensating for the MCMbehavioral affecting parameters to provide normalized MCM behavioralinformation.

The compensation may be executed during step 120, 130, during 140 and/orduring an addition step (now shown). For simplicity of explanation, thecompensation is illustrated as being a part (122) of step 120.

The sensed information may be indicative of component behavioralaffecting parameters and wherein the method may include compensating forthe component behavioral affecting parameters to provide normalizedcomponent behavioral information. The compensation may be executedduring step 120, 130, during 140 and/or during an addition step (nowshown). For simplicity of explanation, the compensation is illustratedas being a part (124) of step 120.

The MCM behavioral affecting parameters may be identified by applyingmachine learning on a vast amount of information acquired by multiplevehicles.

The MCM may be a chassis of a vehicle and the MCM driving events mayinclude at least two of driving on a step, driving in a pothole,serpentine driving, cornering, acceleration squat, breaking, and drivingover a rough road.

The MCM may be a chassis of a vehicle, and the MCM behavioralinformation may represent an impulse response of the vehicle per vehiclespeed for a step-up event and for a step-down event.

The MCM may be a chassis of a vehicle, and the MCM behavioralinformation may represent at least one of:

-   a. a loss of grip of one or more rear wheels during a breaking    event;-   b. a loss of grip of one or more front wheels during a vehicle    acceleration event;-   c. at least one response to a cornering event, the response may be    selected out of (a) a grip loss of wheels of one side of the    vehicle, (b) over-steering and (c) under-steering; and-   d. grip loss per vehicle speed during a driving on a rough road.

The MCM may be a chassis of the vehicle and the determining of thestatus of the one or more MCMs may include calculating multiple scores,the multiple scores may include at least one suspension corneringresponse score, a suspension braking response score; a suspensionacceleration response score; a suspension banking response score; asuspension roughness response score, a suspension serpentine responsescore; a suspension step-up response score; and a suspension step-downresponse score.

The MCM may be a chassis of a vehicle and the one or more component maybe selected out of an air spring, a damper, one or more bushes, and oneor more steering axes.

The one or more component may be an air spring and the determining ofthe status of the one or more component may include calculating an airspring system air leak score, an air spring working area score, and anair spring control performance on excitations score.

The one or more component may be a damper and the determining of thestatus of the one or more component may include calculating a restrainfactor, a velocity response per impulse magnitude, and a compressioncycle versus extension cycle score.

The one or more components may be one or more bushes, and thedetermining of the status of the one or more component may includecalculating a wheel speed difference versus steering wheel positionscore, a steering wheel movements when hitting bump or a pothole score,a steering wheel back1ash or hysteresis score, and a high frequencywheel vibrations score.

The one or more components may be one or more steering axes, and thecomponent behavioral information may include one or more steering axesangles, the one or more steering axes angles may include camber, tow,and castor.

The sensed information may include information about a force applied ona steering wheel of the vehicle. This sensor may provide an indicationof when the driver releases the wheel.

The sensed information may be gained by at least one virtual sensor andat least one actual sensor of a vehicle.

The MCM may be a chassis of the vehicle and the method may includeestimating an aggregated damage to the chassis from multiple directions.

The estimating may be based on sensed information acquired during alifespan (or any other period) of the vehicle.

Health of engine

Engine (combustion) or motor (electric) will be monitored mainly asMCMs:

Using energy equation, analyze the different coefficients/components ofthe equation.

Monitoring and aggregating impacts related to engine/motorperformance—implying cumulating damage.

Logging of significant events

Following characteristics are analyzed, monitored and scored:

Energy Efficiency

Monitor the engine efficiency along time, per different vehicleconfigurations: gear, RPM, load, etc.

Energy equation: efficiency components along time

Symptoms—Engine inefficiency per configuration

Losses due to: rolling resistance, wind drag, engine general health.

Using: Momentary fuel consumption rate (combustion)/current consumption(electric), CoG speed, Road grade, Gear position, Engine RPM, Torquetransfer state, Tire pressure, Weight estimation or at least indicationof change (e.g. door open)

How to detect it:

a. Detect segments of quasi-steady state driving. When all the followingare within some marginal changes: gear, road grade, torque transferstate.

b. Segment duration is 2˜5 [Sec].

c. For each state, generate an instance of the energy equation.

d. Solve the set of equations—per same-weight sessions.

Extract the different coefficients:

1. LUT: inefficiency per gear and RPM

2. Rolling resistance coefficient.

e. Virtual sensor output:

f. LUT and coefficients

-   i. Score the LUT.-   ii. Detect degradations for the specific vehicle, or in comparison    to CVC.

g. Remarks: Some of the coefficients, or their initial value, will begenerated during pre-production process, per CVC. Then, will be saved onboard. Engine efficiency LUT may imply the potential power that can beextracted.

h. If the estimation of the wind drag component fails, using adefault—such as the energy equation at low speeds (i.e. <100 [kph])

Accumulated damage

The method may define the situations/configurations where the engine isworn-out most. Then, accumulate periods times situation-magnitude whensuch situations occur. Accordingly, score the estimated accumulateddamage.

Influencing conditions: Engine load, Engine temperature, and EngineRPM—accumulated

The situations:

-   a. High engine RPM, at too low engine temperature and high load.    Score gets worse at: high RPM, low temperature, high load.-   b. Low engine RPM, at high gear, and high load, close to the point    where auto gear-box is about to switch gear down (engine “ringing”    point). Score gets worse at RPM goes low, and load goes high.

Virtual sensor output:

-   a. Aggregation numbers/counters (similar concept to “Odometer    counter”).-   b. Correlated scores.

Logged Events

Log the situations where extreme conditions happened: Very high RPMduring a long period. Very high RPM at very low engine temperature. Veryhigh engine temperature during long period.

Chassis and Body System

The chassis frame will be monitored mainly as MCMs—Monitoring andaggregating impacts related to chassis completeness and status—implyingcumulated damage, Logging of significant events.

Accumulated Damage

Influencing conditions:

-   a. Impacts—wheels collision at bumps/potholes/sidewalks.-   b. Impacts—body collisions.

Aggregate:

-   a. Abrupt wheel speed changes—score/weight each event with: speed,    wheel-speed change—aggregate per wheel.-   b. Hard acceleration as detected during collision—aggregate per    direction of hit.

Virtual sensor output:

-   a. Per wheel impact damage score-   b. Per body side impact damage score

Logged Events

For each significant collision event as described above, save a record.

Tires

Tires main target is to provide a grip and smooth driving experience.

The tires characterization is done with the following tire parameters:Stiffness,

-   Tread depth, Aquaplaning handling, and Vibrations.-   Brakes

Braking system will be monitored as MCM.

Phenomena: un-even brake disc

Symptom—while braking, the wheels are vibrating due to a-symmetric brakediscs.

How to detect it—while braking, monitor micro changes in wheel speed,correlated with wheel frequency.

-   a. Per wheel.-   b. Rule out rough road conditions.-   c. Correlate with braking pressure.-   d. Virtual sensor output:-   i. Generate a score of braking distance, normalized with brake    pressure.-   ii. Monitor changes in this value.

Phenomena: increasing braking distance

Symptom: While braking, the wheels are vibrating due to a-symmetricbrake discs

How to detect it—while braking, monitor micro changes in wheel speed,correlated with wheel frequency.

-   a. Per wheel.-   b. Rule out rough road conditions.-   c. Correlate with braking pressure.

Virtual sensor output:

-   a. Generate a score of noise/vibration per wheel, normalized with    brake pressure.-   b. Monitor changes in this value.

Ability to brake—per surface friction, weight, road grade, requestedbrake torque.

Steering

Basically, this MCM performance monitor is done as part of thesuspension MCM monitoring.

Logged Events—for each significant collision event as described above,save a record.

What is regarded as significant, high, low, long, short, and how thedamage should accumulate may be learnt over time, during tests, based oninformation provided by vehicle vendor, vehicle manufacturer, garages,and the like.

There may be provided a method for learning how high stress of theengine time segments are applied to accumulated damage.

The method may include collect data of engine malfunction and thehistory of the engine before malfunction.

Collect data from engines that are functioning and estimate theirperformance by tracking their fuel consumption, temperature, generatedtorque and so on.

Learn a state of motor indicating probability of malfunction andperformance.

Collect data for an engine that is outside of the training set use themodel learned to give its state.

FIG. 2A illustrates steps 3312 and 3314 of method 3310 and alsoillustrates a graphic representation of an image of a vehicle 3401surrounded by a curve 3402 that illustrates the damage related to thechassis from each direction. The curve may be replaced by a table or alist of aggregate damage per direction bin.

FIG. 2B illustrates method 3330.

Method 3330 starts by step 3332 of monitoring during duration of life ofvehicle events that reduce the lifespan of the motor.

Step 3332 is followed by step 3334.

Method 2C illustrates method 200 for evaluating a status of a vehicle.

Method 200 may start by step 210 of obtaining sensed informationregarding mechanical forces applied on one or more first components ofthe vehicle, wherein the sensed information is indicative of at least(a) first directions of the forces, and (b) at least other parameterrelated to the forces.

Step 210 may be followed by step 220 of estimating, by a vehiclecomputer, the aggregate damage caused by the mechanical forces to one ormore second components of the vehicle, per each of a plurality of seconddirections.

Step 220 may be followed by step 230 of generating a representation ofthe aggregate vehicle damage each of the plurality of second directionsper location.

Step 210 may include obtaining the sensed information from at least oneactual sensor and at least one virtual sensor.

The one or more second components may include a chassis of the vehicle.

The one or more second components may include a body of the vehicle.

The one or more first components may include vehicle wheels.

The sensed information may include acceleration information.

Determining a Current and Nominal Radius of a Tire.

An ideal tire has a perfect symmetrical shape. An actual tire has ashape that is not perfectly symmetrical—for example—some parts of thetire may be deformed.

It should be noted that, tire deformation is not the common reason fortire asymmetry. The common reason is the existence of the contact patchwhich is strait and not round and the expansion and contraction of thetire that naturally happens while driving.

Given an actual tire—the actual tire may be modeled by an ideal tirethat has an effective radius so that the actual tire circumference ofthe actual tire equals the tire circumference of the ideal tire.

A current effective radius of the tire represents the tire effectiveradius under tire radius affecting parameters conditions (such as tireair pressure, vehicle weight, wheel speed and wheel temperature) thatexist during one or more driving sessions.

A nominal radius of the tire represents the effective radius underpredefined values of wheel radius affecting parameters condition.

A nominal radius may also be referred to astatic loaded radius (akanominal radius) that is the loaded radius of a stationary tire inflatedto the normal recommended pressure.

The actual radius may also be referred to as effective rolling radius(and/or effective tire radius) , Re, is the ratio of the linear velocityof the wheel center in the XSAE direction to the angular velocity of thewheel.

The nominal effective radius may change slowly over time—as the depth ofthe tire threads decrease over time. Thus—the nominal effective radiusmay provide an indication of the depth of the tire threads.

There is a growing need to know at least one out of current effectiveradius of the tire and the nominal effective radius of the tire.

FIG. 3A-3F illustrates various methods for determining effectiveradiuses of tires.

FIG. 3A illustrates method 3100 while FIG. 3B illustrates method 3101.

Method 3100 include steps 3120, 3130, 3140 and 3150.

Step 3120 is followed by step 3130. Steps 3130 and 3140 are followed bystep 3150.

Step 3120 may include performing, by a vehicle, one or more drivingsessions.

Step 3130 may include generating vehicle sensed information thatreflects signals sensed by vehicle sensors (of the vehicle) obtainedduring the one or more driving sessions. The sensed informationreflects, at least, rotational speeds of multiple vehicle wheels.

Step 3140 may include receiving or determining a distance passed by thevehicle during at least one portion of the one or more driving sessions.

Step 3150 may include determining an effective radius of at least onetire of the vehicle based on the distance passed by the vehicle androtational speed of one or more vehicle wheels.

The distance passed by the vehicle may be measured in variousmanners—for example tracking after GPS readings indicative of thelocation of the vehicle, or any other measurement that may provide thedistance passed by the vehicle.

The determination of the distance passed by the vehicle may be providedby a monitor located outside the vehicle—such as traffic cameras,satellite, and the like.

For a certain wheel, an integral over time of the rotational speed ofthe wheel multiplied by the current effective radius of the tire shouldequal the distance passed by the vehicle.

FIG. 3B illustrates method 3101—and shows various steps (3131, 3132,3133, 3134, 3135 and 3136) that may be included in step 3130.

Step 3130 may include at least one out of:

-   a. Pre-processing readings of the rotational speeds of the multiple    vehicle wheels. (3131).-   b. Applying a various filters on at least the readings of the    rotational speeds of the multiple vehicle wheels and other available    vehicle sensors. (3132).-   c. Selecting the at least one portion of the one or more driving    sessions (3134). This may include ignoring information obtained    during curves, accelerations and/or decelerations above thresholds,    sensed information with signal to noise ratio that is below a SNR    threshold, sensed information of a lower that allowed quality, and    the like.-   d. Ignoring sensed information obtained outside the at least one    portion of the one or more driving sessions. (3135).-   e. Pre-processing readings of the rotational speeds of the multiple    vehicle wheels based on road conditions. (3136). This may include    ignoring sensed information obtained during events such as driving    over a bump, entering a hole in the road. Examples of sensing or    knowing the road conditions—including excitations—are provided in    PCT patent application PCT/IB2018/056844 filing date Sep. 7, 2018.

FIG. 3C-3F illustrate methods for determining the impact of each tireradius affecting parameters condition on the current radius of thewheel.

The determination allows to use as reference information about a nominaltire effective radius and normalize each measurement to the nominalcondition.

FIG. 3C illustrates method 3200. Method 3200 includes steps 3220, 3230,3212, 3240 and 3250.

Step 3220 is followed by step 3230 that is followed by step 3212. Steps3212 and 3240 are followed by step 3250.

Step 3220 includes performing, by a vehicle, one or more drivingsessions. A nominal radius of a vehicle tire is known.

Step 3230 includes generating vehicle sensed information that reflectssignals sensed by vehicle sensors (of the vehicle) during parts of theone or more driving sessions in which tire radius all controllableaffecting parameters other than vehicle speed are fixed. The vehiclesensed information reflects, at least rotational speeds of multiplevehicle wheels.

Step 3212 includes determining vehicle speed during the parts of the oneor more driving sessions.

Step 3240 includes receiving or determining a distance passed by thevehicle during the one or more driving sessions.

Step 3250 includes determining a vehicle speed—effective radiusrelationship based on the distance and the vehicle sensed information.

Step 3212 may include at least one out of:

-   a. Select, per sensed event, sensed information regarding one wheel    while ignoring sensed information regarding another wheel. (3213)-   b. Select sensed information regarding a driven wheel while ignoring    sensed information regarding a non-driven wheel. (3214).-   c. Select sensed information regarding a non-driven wheel while    ignoring sensed information regarding a driven wheel. (3216).-   d. Step 3212 may include any combinations of at least wheel speeds    or other sensors of at least one wheel, for example: a . . . b . . .    c . . . .”

FIG. 3D illustrates method 3201. Method 3201 includes steps 3220, 3231,3212, 3240 and 3251.

Step 3220 is followed by step 3231 that is followed by step 3212. Steps3212 and 3240 are followed by step 3251.

Step 3220 includes performing, by a vehicle, one or more drivingsessions. A radius of a vehicle tire is known.

Step 3231 includes generating vehicle sensed information that reflectssignals sensed by vehicle sensors (of the vehicle) during parts of theone or more driving sessions in which a tire radius all controllableaffecting parameters other than tire air pressure and vehicle speed arefixed. The vehicle sensed information reflects, at least, rotationalspeeds of multiple vehicle wheels.

Step 3212 includes determining vehicle speed during the parts of the oneor more driving sessions.

Step 3240 includes receiving or determining a distance passed by thevehicle during the one or more driving sessions.

Step 3251 includes Determining a tire air pressure—effective radiusrelationship based on the distance, the vehicle sensed information andthe vehicle speed—effective radius relationship.

FIG. 3E illustrates method 3202. Method 3202 includes steps 3220, 3232,3212, 3240 and 3252.

Step 3220 is followed by step 3232 that is followed by step 3212. Steps3212 and 3240 are followed by step 3252.

Step 3220 includes performing, by a vehicle, one or more drivingsessions. A radius of a vehicle tire is known.

Step 3232 includes generating vehicle sensed information that reflectssignals sensed by vehicle sensors (of the vehicle) during parts of theone or more driving sessions in which a tire radius all controllableaffecting parameter other than tire temperature and vehicle speed arefixed. The vehicle sensed information reflects, at least, rotationalspeeds of multiple vehicle wheels.

The tire temperature usually stabilizes after driving few minutes.

Different (fixed) tire temperatures may be obtained while driving atdifferent environments and/or at different ambient temperatures.

Step 3212 includes determining vehicle speed during the parts of the oneor more driving sessions.

Step 3240 includes receiving or determining a distance passed by thevehicle during the one or more driving sessions.

Step 3252 includes determining a tire temperature—effective radiusrelationship based on the distance, the vehicle sensed information andthe vehicle speed—effective radius relationship.

FIG. 3F illustrates method 3203. Method 3203 includes steps 3220, 3233,3212, 3240 and 3253.

Step 3220 is followed by step 3233 that is followed by step 3212. Steps3212 and 3240 are followed by step 3253.

Step 3220 includes performing, by a vehicle, one or more drivingsessions. A radius of a vehicle tire is known.

Step 3233 includes generating vehicle sensed information that reflectssignals sensed by vehicle sensors (of the vehicle) during parts of theone or more driving sessions in which a tire radius all controllableaffecting parameter other than vehicle are fixed. The vehicle sensedinformation reflects, at least, rotational speeds of multiple vehiclewheels.

Step 3212 includes determining vehicle speed during the parts of the oneor more driving sessions.

Step 3240 includes receiving or determining a distance passed by thevehicle during the one or more driving sessions.

Step 3253 includes determining a vehicle weight—effective radiusrelationship based on the distance, the vehicle sensed information andthe vehicle speed—effective radius relationship.

Any of the methods of FIGS. 3D-3F may be applied on parameters thatdiffer from those illustrated above and may estimate parameters differfrom these estimated above. These methods may be applied to estimatenon-tested parameters based on “tested parameters” and “non testedparameters” therefore allowing the method to be relevant and applicableto finding the connections between all tire radii influencingparameters.

A non-limiting example of measuring the vehicle weight are provided inPCT patent application PCT/IB2018/056844 filing date Sep. 7, 2018.

FIG. 3G illustrates an example of method 3260 for estimating aneffective radius of a tire of a vehicle.

Step 3260 may start by step 3262 of obtaining sensed information thatreflects (a) a distance passed by the vehicle during one or more drivingsessions, (b) a rotational speed of at least a wheel that comprises thetire during the one or more driving sessions, (c) values of tire radiusaffecting parameters during the one or more driving sessions, whereinthe tire radius affecting parameters comprise a vehicle speed and atleast some other tire radius affecting parameters.

Step 3262 may be followed by step 3266 of selecting at least one portionof the one or more driving sessions.

Step 3266 may include at least one of steps 3133, 3135, and the like.

Step 3266 may include at least one out of:

-   a. Finding segments in which the grip is below a certain threshold    (the threshold be fixed, may be a function of one or more    environmental parameters, and the like) and ignoring these    segments—not selecting them. The length of the segments may range    from less than a meter, a few meters, few tens of meters, and even    more. A segment may span between two different locations values—for    example between two consecutive GPS readings.-   b. Ignoring slip events.-   c. Ignoring portions that comprise slip events.-   d. Selecting at least one portion based on a grip.-   e. Rejecting one or more portions that comprise driving over curves.-   f. Rejecting one or more portions in which the vehicle exhibited    accelerations above acceleration thresholds.-   g. Rejecting one or more portions in which at least a part of the    sensed information had a signal to noise ratio that was below a    signal to noise threshold.-   h. Rejecting one or more portions in which at least a part of the    sensed information had a quality below a quality threshold.-   i. Ignoring rotational speed information obtained during at least    one out of (a) driving over a bump, and (b) entering a hole.-   j. Ignoring sensed information obtained during the one or more    driving sessions but outside the at least one portion.

Step 3266 may be followed by step 3270 of determining the effectiveradius of the tire of the vehicle based on (a) sensed information gainedduring the at least one portion, the sensed information comprises valuesof the tire radius affecting parameters during the at least one portion,and (b) one or more relationships between the effective radius of thetire and tire radius affecting parameters.

Examples of such relationships can be found in steps 3251, 3252 and 3253of FIGS. 3D, 3E and 3F.

Method 3260 may include learning or receiving the relationships betweenthe effective radius of the tire and tire radius affecting parametersduring test driving sessions.

Determining a Depth of Tire Grooves

The depth of the grooves of a tire may be estimated by (a) obtaining areference measurement of the depth of the grooves (for example—gettingthe reference measurement from the specification of a new tire, orphysically measuring the depth of the grooves (Groove_Depth_REF), (b)obtaining a reference measurement of the radius of the tire (forexample—getting the reference measurement from the specification of anew tire) (Tire_Radius_REF), and (c) tracking after changes in adepth-of-grooves-relevant effective (DGRE) radius of the tire—whereasany decrease in the DGRE radius represents a decrement in the depth ofthe grooves.

The current depth of the grooves (Groove_Depth_current) can becalculated by:

Groove_Depth_current=Groove_Depth_REF−(Tire_Radius_REF—DGRE radius ofthe tire).

The DGRE radius of the tire is calculated by ignoring sensed informationobtained by irrelevant road segments. Irrelevant road segments mayinclude road segments in which the temperature of the environmentexceeded a predefined threshold (for example 30, 33, 36 Celsius),segments in which the acceleration/deceleration (absolute value) exceedsa deceleration/deceleration threshold, segments in which the speedexceeds a speed threshold, and the like.

For example—step 3266 mentioned above may include ignoring sensedinformation obtained by irrelevant road segments. Irrelevant roadsegments may include road segments in which the temperature of theenvironment exceeded a predefined threshold (for example 30, 33, 36Celsius), segments in which the acceleration/deceleration (absolutevalue) exceeds a deceleration/deceleration threshold, segments in whichthe speed exceeds a speed threshold, and the like.

Determining a Type of a Tire

Tire types may include all season tires, summer tires, winter tires, andthe like. Each type of tire is manufactured to fit a certain range ofambient conditions (such as a temperature range, rain condition, and thelike.

Tires that fit a certain range of ambient conditions may behave poorlyoutside the certain range of ambient conditions. For example, a summertire may be very stiff at very low temperatures.

There is a growing need to automatically determine the type of tire. Thedetermination may assist in determining vehicle state and limitations(for example available grip, water evacuation, and the like), may assistin automatic functionalities of the vehicle (such as ABS), autonomouslydriving a vehicle, as well as setting limits (suggested limits, orvehicle enforced limits) on the behavior of the vehicle, and the like.

FIGS. 4A-4B illustrates methods 3000 and 3001 for determining a tiretype.

Method 3000 may start by step 3010 of performing a learning process.

Step 3010 may include at least one of steps 3012, 3014, and may includestep 3016.

Step 3012 may include performing multiple driving sessions (while usingdifferent tires in at least some of the driving sessions) whileintroducing different driving conditions.

Thus—one or more driving sessions may be performed using one type oftire while one or more other driving sessions may be performed usinganother type of tire. More than two different types of tires may betested.

The different driving conditions may include different vehicle speed,different paths, different temperatures, different humidity/rain,different speed different gear position, and the like.

Step 3014 may include generating sensed information that reflectssignals sensed by multiple vehicle sensors obtained during the multipledriving sessions.

The sensed signals may include signals obtained from one or more vehiclesensors such as wheel speed sensors, accelerometer, gear sensors, motorsensors, torque sensors, brake sensors, yaw sensors, and the like. Anysensor may be a physical sensor or a virtual sensors. The signals may beprovided from the sensors, vehicle computers or other processors thatpreprocess the signals, and the like. The signals may be obtained, forexample, over one or more communication buses and/or links such as theCANBUS.

Step 3016 may include processing the sensed information to find tiretype distinguishing features that distinguish between one type of tireto another.

A feature may refer to signals related to one or more sensors. A featuremay be generated by applying any function on any group of sensedsignals.

Step 3016 may include applying a classification process (forexample—support vector machine), may include applying a machine learningprocess, and the like.

The learning process 3010 provides tire type distinguishing featuresthat can be used to determine the type of the tire in future drivingsessions.

Thus—step 3010 may be followed by steps 3022, 3024, 3026 and 3028.

Step 3022 may include performing, by a vehicle, one or more new drivingsessions. It should be noted that On an unknown tire that is not part ofthe training set, the method may collect specific data for that tire.That is why may is out of place.

Step 3024 may include generating new sensed information during the oneor more new driving sessions.

Step 3026 may include calculating, based on the new sensed information,at least one values of at least one of the tire types distinguishingfeature.

Step 3028 may include determining a type of tire based on an outcome ofthe calculating.

A feature may represent signals from one or more sensors during shorttime segments (for example of less than a second till few seconds long).

As illustrated in FIG. 4B, step 3014 may include step 3015 of generatingsensed information segments that represents different time windowsduring the multiple driving sessions.

In addition—step 3016 may include step 3017 of processing the sensedinformation segments to find tire type distinguishing features thatdistinguish between one type of tire to another.

Weight Estimation

There may be provide a method for estimating a weight (or a mass) of avehicle.

The weight of the vehicle can be evaluated based on energy consumed bythe vehicle in various path segments and on the energy gained by thefuel consumption.

The weight of the vehicle can be evaluated based on assumptions relatedto a motor efficiency function and a fuel consumption errors associatedwith fuel consumption measurements. The motor efficiency functionrepresents the relationship between consumed fuel and output(mechanical) energy.

The evaluation process may be performed in an iterative manner, in whichone or more assumptions that were used for calculating the evaluatedweight can be re-evaluated given the evaluated weight. The sameiterative approach may be applied to the estimate of the motorefficiency function and/or to the fuel consumption errors.

The evaluation process of the weight and/or any estimation process (thatis based on the estimated weight) can be done at any complexity and/orby any computerized system—either by a vehicle computer or anout-of-vehicle server or computer. For example, the out-of-vehiclecomputer may initially evaluate the energy coefficients using a vastnumber (thousands or more) of measurements. The vehicle computer mayreceive the initially evaluated energy coefficients and use them (inaddition to more measurements obtained by the vehicle sensors) to update(even in real time) the weight estimate of the vehicle.

Initial or other estimates of energy coefficients, motor efficiencyfunction values and/or to the fuel consumption errors values related toa certain vehicle may be based on the behavior of vehicles that belongto the same class of vehicles. A class of vehicles may be vehicles ofthe same model, same manufacturer and/or same year of production.Additionally or alternatively, the vehicle may be classified and/orre-classified based on the weight measurements of the vehicle.

The evaluation process may examine multiple combinations of values ofenergy coefficients, motor efficiency function values and/or to the fuelconsumption errors values.

Any search process may be applied. The entire set of combinations may beevaluated or only a part of the entire set of combination may beevaluated.

The search process may take into account a quality attribute associatedwith a path segment. The quality attribute may be used to filtermeasurements related to path segments and/or may be used in anothermanner.

For example, the weight of a vehicle can be evaluated based on anevaluation of work and energy gained by the fuel consumed.

A weight of a vehicle may be evaluated during a learning period (or alearning period). During the learning period, the monitored vehiclepasses through multiple paths. The multiple paths may be segmented topath segments.

The work and energy gained by the fuel consumed may be estimated pereach path segment.

The method may include obtaining various energy coefficients andperforming various measurements per multiple driving session segment(for example of a length of between a few meters till a few hundredmeters or more).

The values of the energy coefficient values may be evaluated over anyrange and at any resolution. For example—k1 may range between 0 and10,000 and be evaluated at a resolution (steps) of 1000 (0, 1000, 2000,3000 . . . ), and each one of k2, k3 and k4 may range between 0 and 1and be evaluated at a resolution of 0.01 (0, 0.01, 0.02, 0.03 . . . ).

A search process may include calculating the weight estimates for aplurality of path segments. The plurality of path segments may includeall the path segments of all paths that were passed by the vehicleduring the learning period or only some of the paths segments.

A path segments may be ignored (filtered out) for one or more reasonssuch as an insufficient quality.

Non-limiting examples of path segmentation and/or filtering out and/orassigning a quality attribute are provided below:

-   a. Path segment may be relatively short (in time)—for example below    half a minute. The duration of the path is a tradeoff between the    need to obtain enough data, expecting that at least some variables    will be substantially constant during the entire path segment and    having enough valid path segments within a single driving session.    The longer the path segment the higher the quality.-   b. Ignore path segments (or assign low quality) in which the vehicle    descended—thereby avoiding braking losses, and since in such path    segments the fuel is cut from engine.-   c. Motor revolutions per minute (RPM)—higher RPM difference (between    start and end of path segment)—the higher the quality.-   d. Assign higher quality to path segments having bigger momentum    (momentum is the difference between the square of the velocity at    the end of a path segment and the square of the velocity at the    start of a path segment. Insufficient momentum (below a momentum    threshold—such as 50 [m{circumflex over ( )}2/Sec{circumflex over    ( )}4])—may be ignored of—or be assigned a very low quality.-   e. Ignore path segments (or assign low quality)—in which the    velocity exceeded a certain speed threshold (for example—above 70    Km/h)—due to quantization errors.-   f. Ignore path segments (or assign low quality) in which the overall    fuel consumption rate is below a threshold (for example −3 [L/hour])-   g. Assign higher quality level to path segments that include steeper    climbs of the vehicle.-   h. Prevent a single path from including stops that are long enough    to enable substantial weight changes of the vehicles. For example—if    unloading 500 kilograms of goods lasts two minutes—then an    occurrence of such a stop that is at least two minutes long may mark    the border between two path segments.

The suggested weight evaluation is accurate and may require limitedcomputational resources. The suggested weight evaluation compensates forinaccuracies of measurements performed by vehicle sensors and enablesusing low cost limited accuracy sensors.

FIG. 5 illustrates method 300 for evaluating a weight of a vehicle.

Method 300 may start by step 310 of obtaining during a learning periodand by vehicle sensors, vehicle sensor measurements regarding drivingsessions of the vehicle.

Step 310 may be followed by step 320 of calculating, based on thevehicle sensor measurements, an evaluated weight of the vehicle.

The calculating is based on values of energy coefficients that areindicative of energy wasted by the vehicle.

The energy coefficients may include a first energy coefficient (k1) thatis calculated based on grip, tire state and road state.

The first energy coefficient is calculated based on a relationshipbetween grip value and energy waste, a relationship between tire stateand energy waste, and a relationship between road state and energywaste.

The calculating of the first energy coefficient (k1) may include summing(a) a product of the grip (grip_value) multiplied by a grip to energywaste coefficient (k1_g), (b) a product of the tire state (tire_state)and a tire state to energy waste coefficient (k1_t), and (c) a productof the road state and a road state (rr) to energy waste coefficient(k1_r).

The method may include calculating the grip to energy waste coefficient(k1_g), the tire state to energy waste coefficient (k1_t), and the roadstate (rr) to energy waste coefficient (k1_r) using informationgenerated by virtual sensors that comprise a virtual grip sensor, avirtual tire health sensor and a virtual surface event sensor.

The calculating of the second energy coefficient (k2) may includesumming (a) a product of the grip (grip_value) multiplied by a grip toenergy waste coefficient (k2_g), (b) a product of the tire state(tire_state) and a tire state to energy waste coefficient (k2_t), and(c) a product of the road state and a road state (rr) to energy wastecoefficient (k2_r).

The method may include calculating the grip to energy waste coefficient(k2_g), the tire state to energy waste coefficient (k2_t), and the roadstate (rr) to energy waste coefficient (k2_r) using informationgenerated by virtual sensors that comprise a virtual grip sensor, avirtual tire health sensor and a virtual surface event sensor.

The vehicle sensor measurements may include (a) height measurements ofpaths related to the driving sessions, (b) fuel consumption measurementsrelated to the driving sessions, (c) length measurements of the of roadsegments related to the driving sessions; and (d) velocity measurementsrelated to the driving sessions.

The calculating may include searching for values of the energycoefficients that provide at least one distribution related to weightestimates of the vehicle, the at least one distribution fulfills atleast one predefined statistical significance criterion.

The determining of the evaluated weight may include determining weightestimates for each path segment of the plurality of path segments,according to the following equation:

$m = \frac{{{\alpha{me}} \cdot {fe} \cdot {fuel}} - {k_{1}x} - {k_{3}{xc}^{2}}}{{g\Delta h} + \frac{( {{v_{2}}^{2} - {v_{1}}^{2}} )}{2} + {k_{2}x} + {k_{4}{xv}^{2}}}$

wherein the group of energy coefficients further comprises k₂, k₃ andk₄;

-   wherein ame is a value of the estimated motor efficiency function;-   wherein v₂ is a velocity of the vehicle at an end of the path    segment;-   v₁ is a velocity of the vehicle at a start of the path segment;-   v represents at least one value of a velocity of the vehicle when    driving over the path segment;-   x is a length of the path segment;-   Δh is a height difference between the end and the start of the path    segment; and-   fuel is an error corrected fuel consumption related to the path    segment.

The calculating may include searching for values of the energycoefficients that provide at least one distribution related to weightestimates of the vehicle, the at least one distribution fulfills atleast one predefined statistical significance criterion.

The predefined statistical significance criterion is a maximalstatistical significance.

The predefined statistical significance criterion is a smallest standarddeviation of at least one of the weight estimate distributions.

The predefined statistical significance criterion may be a smalleststandard deviation of at least one of the weight estimate distributions.

The determining of the evaluated weight may include determining weightestimates for each path segment of the plurality of path segments andfor different values of the energy coefficients.

The determining of the evaluated weight may include determining weightestimates for each path segment of the plurality of path segments, fordifferent values of the energy coefficients and for different motorefficiency function values.

The determining of the evaluated weight may include determining weightestimates for each path segment of the plurality of path segments, fordifferent values of the energy coefficients and for different fuelconsumption error correction function values.

The determining of the evaluated weight may include determining weightestimates for each path segment of the plurality of path segments, fordifferent values of the energy coefficients, for different motorefficiency function values and for different fuel consumption errorcorrection function values.

The determining of the evaluated weight may include associating aquality attribute with each weight estimate.

The determining of the evaluated weight may include associating aquality attribute with each weight estimate, wherein the at least onedistribution related to weight estimates of the vehicle may beresponsive to the quality attribute assigned to each weight estimate.

The determining of the evaluated weight may include associating aquality attribute with each weight estimate, wherein the at least onedistribution related to weight estimates of the vehicle may be ahistogram, wherein the histogram may include bins, wherein each bin maybe associated with a weight estimate range and has a value thatrepresents quality attributes of weight estimates that belong to thebin.

The determining of the evaluated weight may include associating aquality attribute with each weight estimate, wherein the at least onedistribution related to weight estimates of the vehicle may be ahistogram, wherein the histogram may include bins, wherein each bin maybe associated with a weight estimate range and has a value thatrepresents a sum of quality attributes of weight estimates that belongto the bin.

The method may include assigning quality attributes to at least some ofthe vehicle sensor measurements.

The method may include assigning quality attribute to vehicle sensormeasurements related to a certain path segment based on a differencerelated to velocities of the vehicle at a start and at an end of thecertain path segment.

The method may include assigning quality attribute to vehicle sensormeasurements related to a certain path segment based on a maximalvelocity of the vehicle during the certain driving session.

The method may include ignoring vehicle sensor measurements obtained atpath segments in which the vehicle descended.

The calculating may include applying machine learning.

The determining of the evaluated weight comprises determining weightestimates for each path segment of a plurality of path segments and fordifferent values of the energy coefficients.

The method may include learning the energy coefficients during testdriving sessions.

The energy coefficients further comprise a wind force to energy wastercoefficient (k2′), a constant force constant which can be viewed as a“DC” component and can be calculated when calculating the energycoefficients, a speed constant, and an acceleration of the vehicle.

The determining of the evaluated weight comprises determining weightestimates for each path segment of a plurality of path segments,according to the following equation:

M*a=(k1*Σ_(all wheels)WheelForce)+(k′2*wind_force)−grvity−constant_force−(k′3*speed)−k2*M.

Wherein M is a mass of the vehicle, WheelForce is . . . , wind_force isa function of air resistance to the vehicle, WheelForce is the drivingforce of the car at the tire, wind_force is a function of air resistanceto the vehicle, constant_force is independent and constant loses likeinternal friction.

The wind_force may equal 0.22*calc_air_density*(speed/3.6)²*1.5, whereascalc_air_density equals1.201*{290*(air_pressure−0.378*relative_humidity)}/{1000*(air_temperature+273.15)}/100.Other values and other functions may be used.

The energy coefficients may be learnt during test driving sessions.

FIG. 6 illustrates a vehicle 600 and a computerized system 620 locatedoutside the vehicle.

The vehicle may include physical sensors 602, virtual sensors 604, acomputer (and/or a processor and/or a control unit) 606, MCMs 608,components 610 and memory unit 612 for storing information and/ormetadata 614—such as any data structure illustrated in thespecification. The computerized system 620 may any data structureillustrated in the specification.

The computer 606 and/or the computerized system 620 is configured toexecute any of the mentioned above methods.

There may be provided a method for evaluating a status of a vehicle, themethod may include obtaining sensed information during one or moredriving sessions of the vehicle; determining, based on the sensedinformation, (a) multi-component-model (MCM) behavioral informationregarding one or more MCM driving events, and (b) a component behavioralinformation regarding one or more component driving events; wherein abehavior of at least one first part of the vehicle during the one ormore MCM driving events is indicative of a status of one or more MCMs;wherein a behavior of at least one second part of the vehicle during theone or more component driving event is indicative of a status of one ormore components; determining the status of the one or more MCMs, basedat least on the MCM behavioral information; and determining the statusof the one or more components, based at least on the componentbehavioral information, the status of the component.

There may be provided a non-transitory computer readable medium forevaluating a status of a vehicle, the non-transitory computer readablemedium may store instructions for obtaining sensed information duringone or more driving sessions of the vehicle; determining, based on thesensed information, (a) multi-component-model (MCM) behavioralinformation regarding one or more MCM driving events, and (b) acomponent behavioral information regarding one or more component drivingevents; wherein a behavior of at least one first part of the vehicleduring the one or more MCM driving events is indicative of a status ofone or more MCMs; wherein a behavior of at least one second part of thevehicle during the one or more component driving event is indicative ofa status of one or more components; determining the status of the one ormore MCMs, based at least on the MCM behavioral information; anddetermining the status of the one or more components, based at least onthe component behavioral information, the status of the component.

There may be provided a system for evaluating a status of a vehicle, thesystem may include at least one processor configured to obtain sensedinformation during one or more driving sessions of the vehicle;determine, based on the sensed information, (a) multi-component-model(MCM) behavioral information regarding one or more MCM driving events,and (b) a component behavioral information regarding one or morecomponent driving events; wherein a behavior of at least one first partof the vehicle during the one or more MCM driving events is indicativeof a status of one or more MCMs; wherein a behavior of at least onesecond part of the vehicle during the one or more component drivingevent is indicative of a status of one or more components; determine thestatus of the one or more MCMs, based at least on the MCM behavioralinformation; and determine the status of the one or more components,based at least on the component behavioral information, the status ofthe component.

There may be provided a method for evaluating a status of a vehicle, themethod may include obtaining sensed information regarding mechanicalforces applied on one or more first components of the vehicle, whereinthe sensed information is indicative of at least (a) first directions ofthe forces, and (b) at least other parameter related to the forces;estimating, by a vehicle computer, the aggregate damage caused by themechanical forces to one or more second components of the vehicle, pereach of a plurality of second directions; and generating arepresentation of the aggregate vehicle damage each of the plurality ofsecond directions per location.

There may be provided a non-transitory computer readable medium forevaluating a status of a vehicle, the non-transitory computer readablemedium may store instructions for obtaining sensed information regardingmechanical forces applied on one or more first components of thevehicle, wherein the sensed information is indicative of at least (a)first directions of the forces, and (b) at least other parameter relatedto the forces; estimating, by a vehicle computer, the aggregate damagecaused by the mechanical forces to one or more second components of thevehicle, per each of a plurality of second directions; and generating arepresentation of the aggregate vehicle damage each of the plurality ofsecond directions per location.

There may be provided a system for evaluating a status of a vehicle, thesystem may include at least one processor configured to obtain sensedinformation regarding mechanical forces applied on one or more firstcomponents of the vehicle, wherein the sensed information is indicativeof at least (a) first directions of the forces, and (b) at least otherparameter related to the forces; estimate the aggregate damage caused bythe mechanical forces to one or more second components of the vehicle,per each of a plurality of second directions; and generate arepresentation of the aggregate vehicle damage each of the plurality ofsecond directions per location.

There may be provided a method for estimating an effective radius of atire of a vehicle, the method may include obtaining sensed informationthat reflects (a) a distance passed by the vehicle during one or moredriving sessions, (b) a rotational speed of at least a wheel that mayinclude the tire during the one or more driving sessions, (c) values oftire radius affecting parameters during the one or more drivingsessions, wherein the tire radius affecting parameters comprise avehicle speed and at least some other tire radius affecting parameters;selecting at least one portion of the one or more driving sessions; anddetermining the effective radius of the tire of the vehicle based on (a)sensed information gained during the at least one portion, the sensedinformation may include values of the tire radius affecting parametersduring the at least one portion, and (b) one or more relationshipsbetween the effective radius of the tire and tire radius affectingparameters.

There may be provided a non-transitory computer readable medium forestimating an effective radius of a tire of a vehicle, thenon-transitory computer readable medium may store instructions forobtaining sensed information that reflects (a) a distance passed by thevehicle during one or more driving sessions, (b) a rotational speed ofat least a wheel that may include the tire during the one or moredriving sessions, (c) values of tire radius affecting parameters duringthe one or more driving sessions, wherein the tire radius affectingparameters comprise a vehicle speed and at least some other tire radiusaffecting parameters; selecting at least one portion of the one or moredriving sessions; and determining the effective radius of the tire ofthe vehicle based on (a) sensed information gained during the at leastone portion, the sensed information may include values of the tireradius affecting parameters during the at least one portion, and (b) oneor more relationships between the effective radius of the tire and tireradius affecting parameters.

The terms “including”, “comprising”, “having”, “consisting” and“consisting essentially of” are used in an interchangeable manner. Forexample—any method may include at least the steps included in thefigures and/or in the specification, only the steps included in thefigures and/or the specification.

In the foregoing specification, the invention has been described withreference to specific examples of embodiments of the invention. It will,however, be evident that various modifications and changes may be madetherein without departing from the broader spirit and scope of theinvention as set forth in the appended.

Moreover, the terms “front,” “back,” “rear” “top,” “bottom,” “over,”“under” and the like in the description and in the claims, if any, areused for descriptive purposes and not necessarily for describingpermanent relative positions. It is understood that the terms so usedare interchangeable under appropriate circumstances such that theembodiments of the invention described herein are, for example, capableof operation in other orientations than those illustrated or otherwisedescribed herein.

The connections as discussed herein may be any type of connectionsuitable to transfer signals from or to the respective nodes, units, ordevices, for example via intermediate devices. Accordingly, unlessimplied or stated otherwise, the connections may for example be directconnections or indirect connections. The connections may be illustratedor described in reference to being a single connection, a plurality ofconnections, unidirectional connections, or bidirectional connections.However, different embodiments may vary the implementation of theconnections. For example, separate unidirectional connections may beused rather than bidirectional connections and vice versa. Also,plurality of connections may be replaced with a single connection thattransfers multiple signals serially or in a time multiplexed manner.Likewise, single connections carrying multiple signals may be separatedout into various different connections carrying subsets of thesesignals. Therefore, many options exist for transferring signals.

Although specific conductivity types or polarity of potentials have beendescribed in the examples, it will be appreciated that conductivitytypes and polarities of potentials may be reversed.

Those skilled in the art will recognize that the boundaries betweenvarious components are merely illustrative and that alternativeembodiments may merge various components or impose an alternatedecomposition of functionality upon various components. Thus, it is tobe understood that the architectures depicted herein are merelyexemplary, and that in fact many other architectures can be implementedwhich achieve the same functionality.

Any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” Each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected,” or“operably coupled,” to Each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundariesbetween the above described operations merely illustrative. The multipleoperations may be combined into a single operation, a single operationmay be distributed in additional operations and operations may beexecuted at least partially overlapping in time. Moreover, alternativeembodiments may include multiple instances of a particular operation,and the order of operations may be altered in various other embodiments.

However, other modifications, variations and alternatives are alsopossible. The specifications and drawings are, accordingly, to beregarded in an illustrative rather than in a restrictive sense.

Any combination of any component of any component and/or unit of asystem or device or apparatus that is illustrated in any of the figuresand/or specification and/or the claims may be provided.

Any combination of any systems and/or devices and/or apparatusesillustrated in any of the figures and/or specification and/or the claimsmay be provided.

Any combination of steps, operations and/or methods illustrated in anyof the figures and/or specification and/or the claims may be provided.

Any combination of operations illustrated in any of the figures and/orspecification and/or the claims may be provided.

Any combination of methods illustrated in any of the figures and/orspecification and/or the claims may be provided.

There may be provided one or more non-transitory computer readablemedium that may store instructions for executing combination of steps,operations and/or methods illustrated in any of the figures and/orspecification and/or the claims.

There may be provided one or more apparatuses and/or systems and/orunits that are constructed and arranged to execute any combination ofsteps, operations and/or methods illustrated in any of the figuresand/or specification and/or the claims.

The terms configured to and constructed and arranged to are used in aninterchangeable manner.

The phrase “may be X” indicates that condition X may be fulfilled. Thisphrase also suggests that condition X may not be fulfilled.

Those skilled in the art will recognize that the boundaries betweenlogic blocks are merely illustrative and that alternative embodimentsmay merge logic blocks or circuit elements or impose an alternatedecomposition of functionality upon various logic blocks or circuitelements. Thus, it is to be understood that the architectures depictedherein are merely exemplary, and that in fact many other architecturescan be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected,” or“operably coupled,” to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundariesbetween the above described operations merely illustrative. The multipleoperations may be combined into a single operation, a single operationmay be distributed in additional operations and operations may beexecuted at least partially overlapping in time. Moreover, alternativeembodiments may include multiple instances of a particular operation,and the order of operations may be altered in various other embodiments.

Also for example, in one embodiment, the illustrated examples may beimplemented as circuitry located on a single integrated circuit orwithin a same device. Alternatively, the examples may be implemented asany number of separate integrated circuits or separate devicesinterconnected with each other in a suitable manner.

Also for example, the examples, or portions thereof, may implemented assoft or code representations of physical circuitry or of logicalrepresentations convertible into physical circuitry, such as in ahardware description language of any appropriate type.

Also, the invention is not limited to physical devices or unitsimplemented in non-programmable hardware but can also be applied inprogrammable devices or units able to perform the desired devicefunctions by operating in accordance with suitable program code, such asmainframes, minicomputers, servers, workstations, personal computers,notepads, personal digital assistants, electronic games, automotive andother embedded systems, cell phones and various other wireless devices,commonly denoted in this application as ‘computer systems’.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word ‘comprising’ does notexclude the presence of other elements or steps than those listed in aclaim. Furthermore, the terms “a” or “an,” as used herein, are definedas one or more than one. Also, the use of introductory phrases such as“at least one” and “one or more” in the claims should not be construedto imply that the introduction of another claim element by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim element to inventions containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an.”The same holds true for the use of definite articles. Unless statedotherwise, terms such as “first” and “second” are used to arbitrarilydistinguish between the elements such terms describe. Thus, these termsare not necessarily intended to indicate temporal or otherprioritization of such elements. The mere fact that certain measures arerecited in mutually different claims does not indicate that acombination of these measures cannot be used to advantage.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

We claim:
 1. A method for estimating an effective radius of a tire of avehicle, the method comprises: obtaining sensed information thatreflects (a) a distance passed by the vehicle during one or more drivingsessions, (b) a rotational speed of at least a wheel that comprises thetire during the one or more driving sessions, (c) values of tire radiusaffecting parameters during the one or more driving sessions, whereinthe tire radius affecting parameters comprise a vehicle speed and atleast some other tire radius affecting parameters; selecting at leastone portion of the one or more driving sessions; and determining theeffective radius of the tire of the vehicle based on (a) sensedinformation gained during the at least one portion, the sensedinformation comprises values of the tire radius affecting parametersduring the at least one portion, and (b) one or more relationshipsbetween the effective radius of the tire and tire radius affectingparameters.
 2. The method according to claim 1 comprising ignoringsensed information obtained during the one or more driving sessions butoutside the at least one portion.
 3. The method according to claim 1wherein the selecting of the at least portion comprises ignoring slipevents.
 4. The method according to claim 1 wherein the selecting of theat least portion comprises ignoring portions that comprise slip events.5. The method according to claim 1 comprising selecting at least oneportion based on a grip.
 6. The method according to claim 1 comprisingrejecting one or more portions that exhibit a grip that is below a gripthreshold.
 7. The method according to claim 1 comprising rejecting oneor more portions that comprise driving over curves.
 8. The methodaccording to claim 1 comprising rejecting one or more portions in whichthe vehicle exhibited accelerations above acceleration thresholds. 9.The method according to claim 1 comprising rejecting one or moreportions in which at least a part of the sensed information had a signalto noise ratio that was below a signal to noise threshold.
 10. Themethod according to claim 1 comprising rejecting one or more portions inwhich at least a part of the sensed information had a quality below aquality threshold.
 11. The method according to claim 1 comprisingignoring rotational speed information obtained during at least one outof (a) driving over a bump, and (b) entering a hole.
 12. The methodaccording to claim 1 comprising learning the relationships between theeffective radius of the tire and tire radius affecting parameters duringtest driving sessions.
 13. The method according to claim 12 wherein theone or more relationships comprise a vehicle speed—effective radiusrelationship that is learnt during first test driving sessions, a tireair pressure—effective radius relationship that is learnt during secondtest driving sessions, a vehicle weight—effective radius relationshipthat is learnt during third test driving sessions, and vehicleweight—effective radius relationship that is learnt during first fourthdriving sessions.
 14. The method according to claim 1 comprisingselecting, per sensed event, sensed information regarding one wheelwhile ignoring sensed information regarding another wheel.
 15. Themethod according to claim 1 comprising monitoring a change of thenominal effective radius over time and determining, based on the change,a depth of tire threads.
 16. The method according to claim 1 wherein thedistance passed by the vehicle is provided by a monitor located outsidethe vehicle.
 17. The method according to claim 16 wherein the monitor atraffic camera.
 18. The method according to claim 16 wherein the monitoris a satellite.
 19. A non-transitory computer readable medium forestimating an effective radius of a tire of a vehicle, thenon-transitory computer readable medium stores instructions for:obtaining sensed information that reflects (a) a distance passed by thevehicle during one or more driving sessions, (b) a rotational speed ofat least a wheel that comprises the tire during the one or more drivingsessions, (c) values of tire radius affecting parameters during the oneor more driving sessions, wherein the tire radius affecting parameterscomprise a vehicle speed and at least some other tire radius affectingparameters; selecting at least one portion of the one or more drivingsessions; and determining the effective radius of the tire of thevehicle based on (a) sensed information gained during the at least oneportion, the sensed information comprises values of the tire radiusaffecting parameters during the at least one portion, and (b) one ormore relationships between the effective radius of the tire and tireradius affecting parameters.
 20. A system for estimating an effectiveradius of a tire of a vehicle, the system comprises at least oneprocessor configured to: obtain sensed information that reflects (a) adistance passed by the vehicle during one or more driving sessions, (b)a rotational speed of at least a wheel that comprises the tire duringthe one or more driving sessions, (c) values of tire radius affectingparameters during the one or more driving sessions, wherein the tireradius affecting parameters comprise a vehicle speed and at least someother tire radius affecting parameters; select at least one portion ofthe one or more driving sessions; and determine the effective radius ofthe tire of the vehicle based on (a) sensed information gained duringthe at least one portion, the sensed information comprises values of thetire radius affecting parameters during the at least one portion, and(b) one or more relationships between the effective radius of the tireand tire radius affecting parameters.